One of the common considerations when prescribing haloperidol is whether it will prolong the QT interval. This is a measure of the heart rhythm on the EKG that correlates with one’s risk for serious arrhythmias such as torsades de pointes.


Earlier this year, van den Boogaard et al published one of the largest RCTs to compare haloperidol against placebo (700+ people in both groups).

Their main finding was that prophylactic haloperidol was not helpful for reducing the rate of delirium or improving mortality.

But one of their most interesting results was the safety data. This showed that their dose of haloperidol had no effect on the QT interval and caused no increased rates of extrapyramidal symptoms. Their regimen was haloperidol IV 2 mg every 8 hours, which is equivalent to ~ 10 mg oral haloperidol in one day.

The maximum QT interval was 465 ms in the 2 mg haloperidol group and 463 ms in the placebo group, a non-significant difference with a 95% CI for the difference of -2.0 to 5.0.

Notably, they excluded people with acute neurologic conditions (who may have been more likely to have cardiovascular problems) and people with QTc already > 500 ms, which makes generalization of this finding to those groups a bit tricky.


Since I did the same analysis for antidepressants yesterday, I figured that I would analyze the receptor binding profiles of antipsychotics today. Here is a visualization:

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And here is a dendrogram based on a clustering of those receptor affinities:

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It turns out that it’s much harder to see a trend in which these classes cluster based on chemical structure like the antidepressants did, but perhaps you will be able to notice some trends:

Here’s my code to reproduce this.

As I’m trying to learn more about antidepressants, I found it interesting to make a visualization of the receptor binding profiles of some of the better characterized ones, so I thought I would post it here.

Antidepressant receptor binding

Some of these medications aren’t widely used anymore or were never pursued for development, so they are also a window into the history of psychiatry and what could have been. This is how the meds cluster based on their receptor binding:

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One interesting thing about these clusters is that they cut the medications into groups distinguished by their chemical/drug classes:

  • Group #1: TeCAs like mirtazapine and one TCA, doxepin; o
  • Group #2: TCAs like amitryptiline and one TeCA, amoxapine
  • Group #3: SSRIs/SNRIs, like fluoxetine and venlafaxine
  • Group #4: Phenylpiperazines, like trazodone
  • Group #5: NRIs/NDRIs, like atomoxetine and buproprion

Here’s my code to reproduce this.



As one of my manifestations of intellectual contrarianism, I like to collect historical examples of times when a largish group of scientists thought that a complicated theory was the best way to explain a set of facts, but then a more simple explanation turned out to be much better.

I especially like examples of this in neuroscience, where people are wont to postulate complicated theories about the way that we think.

There is perhaps no better example than the debate between the reticular theory of the nervous system and the neuron doctrine.

The reticular theory postulated a form of exceptionalism in the nervous system: that axons and dendrites seen on light microscopy were not attached to cells but were in fact a separate, non-cellular entity, forming their own protoplasmic network.

The neuron doctrine is, at least in hindsight, much simpler, postulating that axons and dendrites are extensions of cells, as occurs in other types of biology.


Cajal’s drawing of neurons in the chick cerebellum, from Wikipedia

The reticular theory had many proponents, including Camillo Golgi and Franz Nissl, and lasted from 1840-1935. It’s easy to dismiss it now, but it was a reasonable idea at the time.

Now, though, it’s an good example of how theories that postulate that the brain is extremely complicated and different than other types of biology do not have a good track record.

In the past 20 years, deep brain stimulation (DBS) has been used for over 100,000 patients with Parkinson’s disease. The success of this procedure has led investigators to try DBS for other neurologic conditions, such as Alzheimer’s disease (AD).

In 2016, Lozano et al reported on one of the largest trials for DBS in AD, the “ADvance” trial, in which they targeted the fornix, a bundle of nerve fibers in the center of the brain that is the major output tract of the hippocampus.


This was a well-run, double-blind, randomized study. One of the nice aspects about brain stimulation trials is the ease of performing a sham stimulation arm. That is, treatment can be randomly turned either “on” and “off” for a period of time, allowing a subset of participants to serve as controls (stimulation turned “off”) for a period of time before they actually do get the stimulation (stimulation turned “on”) in case it is actually helpful.

In terms of the trial results, one of the patients (out of 42) had an implant infection. Overall, the trial did not show a significant benefit mitigating the decline in ADAS-13 or CDR-SB scores (measures of cognitive function):

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Lozano et al 2016; doi: 10.3233/JAD-160017

While this trial did not show efficacy at their sample sizes, personally I expect that DBS for early AD could work to at least alleviate symptoms, if the right circuits were targeted at the right time.

My reasoning here is that we know that a few other cognitive strategies can help slow the course of AD, including processing speed training and acetylcholinesterase inhibitors.

There are at least 4 active DBS trials for AD on clinicaltrials.gov:

It will be interesting to monitor this growing field in the coming years.

Microglia can last a lifetime

An important paper from Füger et al last month, in which they labelled individual microglia in mouse brains and tracked their locations over 1.5 years. Here were some of their major findings:

  • The median lifespan of microglia was estimated to be approximately 2.5 years, which is close to the mean lifespan of the mice that they were studying. So, it is fair to think of microglia as long-lived tissue macrophages. It is also clear how changes in microglia epigenetics in earlier life could affect late-life cognitive outcomes.
  • Microglia died at a higher rate in older mice, suggesting that aging may lead to alterations in microglia function that could affect neurodegenerative disease.
  • In APPPS1 mice, microglia proliferate 3x more than usual in areas of the cortex without amyloid plaque, but only proliferate a normal amount in areas of the cortex with amyloid plaque. This suggests that any increase in microglia near plaque is likely due to migration, not local proliferation.

An interesting study from Risacher et al splits ADNI participants into three subtypes of Alzheimer’s, based on whether their baseline atrophy was more severe in the hippocampus and/or cortex. These groups were previously defined based on where in the brain pathologic tau deposits are predominant on postmortem exam, but the authors adapted them to the MRI level. Here were their definitions:

  • Hippocampal sparing (HpSpMRI) = Hippocampal volume:Cortical volume ratio > 75th percentile, Hippocampal volume > median, Cortical volume < median. (n = 33)
  • Limbic predominant (LPMRI) = Hippocampal volume:Cortical volume ratio < 25th percentile, Hippocampal volume < median, Cortical volume > median.  (n = 38)
  • Typical AD (tADMRI) = all other participants (n = 158)

For participants who had 24 month longitudinal data, they found that the hippocampal sparing subtype had the worst progression of cognitive decline, despite a similar baseline cognition profile:

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Figure 2C from PMID: 29070667